本文關(guān)鍵詞： OrientDB 知識(shí)圖譜 本體　出處：《重慶大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：公安情報(bào)工作的重點(diǎn)是關(guān)注人物、組織、賬號(hào)之間的關(guān)系,在實(shí)際工作中往往需要通過一個(gè)姓名獲得與之相關(guān)的所有信息,比如某人最近和哪些人聯(lián)系過,某人參加過哪些活動(dòng),某人使用過哪些社交賬號(hào)等,這些需求通常需要人工在海量信息中查找答案,于是論文提出構(gòu)建一套描述了人物之間的關(guān)聯(lián)關(guān)系的知識(shí)圖譜,使得通過查詢知識(shí)圖譜中人物關(guān)系,就能獲得人物的基本信息、人物相關(guān)活動(dòng)軌跡信息、人物的相關(guān)人物信息等。當(dāng)然,知識(shí)圖譜應(yīng)用在查詢?nèi)宋镪P(guān)系上為情報(bào)工作帶來了便利,但是如何設(shè)計(jì)和構(gòu)建知識(shí)圖譜卻是一個(gè)難點(diǎn)。然而,現(xiàn)存的許多研究工作都假設(shè)了原始數(shù)據(jù)已經(jīng)清洗完畢,人物關(guān)系已經(jīng)構(gòu)建成為三元組數(shù)據(jù),甚至知識(shí)圖譜已經(jīng)構(gòu)建完畢,而主要研究知識(shí)圖譜的分析方法和應(yīng)用場(chǎng)景。于是,論文的主要工作集中在從原始數(shù)據(jù)到形成人物知識(shí)圖譜的過程上,而對(duì)于人物知識(shí)圖譜應(yīng)用只需要滿足查詢?nèi)宋镪P(guān)系的要求。對(duì)于設(shè)計(jì)構(gòu)建人物關(guān)系的知識(shí)圖譜,主要存在三個(gè)難點(diǎn)問題:一、原始的數(shù)據(jù)量非常大而且數(shù)據(jù)結(jié)構(gòu)完全不一樣,如何從中抽取到人物、組織、賬號(hào)等關(guān)注的對(duì)象,以及如何判斷兩個(gè)人物存在關(guān)系。二、針對(duì)知識(shí)圖譜的更新問題,如何判斷新加入的人物是否已經(jīng)存在于知識(shí)圖譜中,而且如果對(duì)于已存在的人物又如何合并人物相關(guān)信息。三、人物關(guān)系包含了人與人、人與組織、人與網(wǎng)站、人與賬號(hào)等上千類關(guān)系,如何設(shè)計(jì)每種對(duì)象的數(shù)據(jù)模型,既能描述對(duì)象基本信息,又能描述對(duì)象之間關(guān)系。本文的主要工作有:(1)在本體建模的基礎(chǔ)上,提出了人物關(guān)系建模方法。首先根據(jù)域、類、屬性、實(shí)體的定義,詳細(xì)設(shè)計(jì)了這四類數(shù)據(jù)結(jié)構(gòu),并指導(dǎo)創(chuàng)建了人物屬性集合、人物關(guān)系集合,并實(shí)際驗(yàn)證了該建模方案的可行性。(2)在自然語(yǔ)言分詞技術(shù)基礎(chǔ)上,提出了融合多正則表達(dá)式的人物實(shí)體抽取技術(shù)。通過實(shí)驗(yàn)比較了中科院分詞和哈工大分詞的中文分詞效果,分析了兩種分詞技術(shù)的不同特點(diǎn)。同時(shí),實(shí)驗(yàn)證明了結(jié)合多正則表達(dá)式可以提高實(shí)體抽取效果,特別適用于識(shí)別賬號(hào)類實(shí)體。(3)提出了基于知識(shí)圖譜的人物關(guān)系搜索、語(yǔ)義搜索、場(chǎng)景化搜索這三種應(yīng)用方案,并對(duì)比了三種方案的應(yīng)用場(chǎng)景。
[Abstract]:The focus of public security intelligence work is to focus on the relationships between people, organizations, and accounts. In practical work, it is often necessary to obtain all the relevant information through a name, such as who someone has recently contacted. As to what activities someone has participated in, what social accounts they have used, and so on, which needs to be manually searched for answers in a huge amount of information, the paper proposes to build a knowledge map that describes the relationships between people. It makes it possible to obtain the basic information of the characters, the information about the trajectory of the activities of the characters, the information about the characters, and so on by querying the relationships of the characters in the knowledge map. Of course, The application of knowledge atlas in querying relationships of people brings convenience to intelligence work, but how to design and construct knowledge atlas is a difficulty. However, many existing researches assume that the original data has been cleaned. The relationship between people has been constructed into triple data, even the knowledge map has been constructed, and the analysis methods and application scenarios of knowledge map are mainly studied. The main work of this paper is to focus on the process from the original data to the formation of the character knowledge map, but the application of the character knowledge map only needs to meet the requirements of querying the relationship between the people, and to design and construct the knowledge map of the person relationship, There are mainly three difficult problems: first, the original data volume is very large and the data structure is completely different, how to extract the objects of concern such as characters, organizations, accounts, and how to judge the relationship between the two characters. In view of the problem of updating the knowledge map, how to judge whether the newly added characters already exist in the knowledge map, and how to combine the relevant information about the existing characters. Third, the relationship of people includes people, people and organizations. How to design the data model of each object can describe the basic information of the object and the relationship between the objects. The main work of this paper is: 1) on the basis of ontology modeling, how to design the data model of each object, such as human and website, person and account number, etc. Firstly, according to the definition of domain, class, attribute and entity, four kinds of data structures are designed in detail. Finally, the feasibility of the modeling scheme is verified. (2) based on the natural language word segmentation technology, a character entity extraction technique combining multiple regular expressions is put forward, and the Chinese word segmentation effect of Chinese Academy of Sciences segmentation and Hart participle is compared through experiments. This paper analyzes the different characteristics of two word segmentation techniques. At the same time, the experiment proves that the combination of multiple regular expressions can improve the effect of entity extraction, especially for identifying account class entities. Scene-based search for these three application schemes, and compare the application scenarios of the three schemes.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：D035.3

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